Among various natural disasters, such as storms, drought, floods, landslides and tsunamis (seismic sea waves), forest fires rank high as a natural disaster that causes huge losses of both life and property and generates ecological problems. Accordingly, an early warning of a forest fire is crucial for a reduction in the possible loss of both life and property. Recently, thanks to the rapid advancement of information technology, automatic forest fire detection has become a new research field.
A conventional method for detecting a forest fire is to detect a fire or smoke with the naked eye. Currently, in order to overcome the problems of the conventional method, Infrared (IR) sensors and a Light Detection And Ranging (LIDAR) system are used. They detect a forest fire by detecting the heat flux of flame light and measuring laser light generated by back scattering via smoke particles. However, this optical sensor-based forest fire detection method is problematic in that many erroneous warnings are issued because of atmospheric conditions or the reflection of light, and in that there is a large distance between a sensor and the starting point of a fire. Recently, a forest fire detection method using a Charge-Coupled Device (CCD) camera has been widely used because of the advantages in which equipment and management costs are low, a single camera can cover a wide range, the response time for the detection of a fire and smoke is short, and an administrator can detect a fire and also monitor the states of flame and smoke without visiting a corresponding location. However, this method still has the problems of an illuminance environment, variations in the color of smoke, and low-quality outdoor space images.
In general, the detection of a forest fire may be divided into two categories: smoke detection and flame detection. Since, during the development of a forest fire, smoke is generated prior to flame, it is particularly important to rapidly and accurately detect smoke in order to issue an early warning of a forest fire. Currently, various methods for detecting smoke have been researched.
Töreyin and Cetin proposed a part-based smoke detection algorithm using four sub-algorithms (see B. U. Töreyin and A. E. Cetin, “Wildfire Detection using LMS based Active Learning,” IEEE International Conference on Acoustics, Speech and Signal Processing, 1461-1464, 2009). That is, in this algorithm, four sub-algorithms, including (a) a sub-algorithm for detecting a slow-moving video object, (b) a sub-algorithm for detecting a gray region, (c) a sub-algorithm for detecting a rising object, and (d) a sub-algorithm for removing a shadow, separately detect the presence of smoke, and the determinations of the sub-algorithms are combined with each other by an adaptive weighted majority algorithm.
Ham et al. proposed an algorithm for detecting the smoke of a forest fire using Fuzzy Finite Automata (FFA) and visual features (see S. J. Ham, B. C. Ko, and J. Y. Nam, “Vision based Forest Smoke Detection using Analyzing of Temporal Patterns of Smoke and their Probability Models,” Image Processing: Machine Vision Applications IV, 7877:1-6, 2011).
However, the above proposed methods are problematic in that they confuse a moving object, such as a moving cloud or a swaying tree, with smoke, thus frequently resulting in the case in which they cannot accurately detect actual smoke and thus provide false information.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure.